Nonlinear Time Series Prediction by Weighted Vector Quantization (2003)  (Make Corrections)  
A. Lendasse, D. Francois, V. Wertz, M. Verleysen

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Abstract: Classical nonlinear models for time series prediction exhibit improved capabilities compared to linear ones. Nonlinear regression has however drawbacks, such as overfitting and local minima problems, user-adjusted parameters, higher computation times, etc. There is thus a need for simple nonlinear models with a restricted number of learning parameters, high performances and reasonable complexity. In this paper, we present a method for nonlinear forecasting based on the quantization of... (Update)

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BibTeX entry:   (Update)

@misc{ lendasse-nonlinear,
  author = "A. Lendasse and D. Francois and V. Wertz and M. Verleysen",
  title = "Nonlinear Time Series Prediction by Weighted Vector Quantization",
  url = "citeseer.comp.nus.edu.sg/684624.html" }
Citations (may not include all citations):
168   Times Series Prediction: Forecasting the future and Understa.. (context) - Weigend, Gershenfeld - 1994
147   Springer Series in Information Sciences (context) - Kohonen - 2001
130   Vector Quantization (context) - Gray - 1984
127   Oscillations and Chaos in Physiological Control Systems (context) - Mackey, Glass - 1977
17   Using the SOM and Local Models in Time Series Prediction - Vesanto - 1997
6   Width optimization of the Gaussian kernels in Radial Basis F.. - Benoudjit, Archambeau et al. - 2002
4   Lazy Learning for Local Modeling and Control Design - Bontempi, Birattari et al. - 1998
2   Approximation by RadialBasis Function networks -- Applicatio.. - Lendasse, Lee et al.
1   analyse des correspondences et methodes neuronales (context) - Ibbou - 1998

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